Algorithms for clustering data
Algorithms for clustering data
A computer generated aid for cluster analysis
Communications of the ACM
Visualizing Data
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Approximate data mining in very large relational data
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Scalable visual assessment of cluster tendency for large data sets
Pattern Recognition
Linear manifold clustering in high dimensional spaces by stochastic search
Pattern Recognition
Tendency curves for visual clustering assessment
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Improvement of Jarvis-Patrick Clustering Based on Fuzzy Similarity
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
An algorithm for clustering tendency assessment
WSEAS Transactions on Mathematics
Is VAT really single linkage in disguise?
Annals of Mathematics and Artificial Intelligence
Relational generalizations of cluster validity indices
IEEE Transactions on Fuzzy Systems
Visualization of single clusters
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
iVAT and aVAT: enhanced visual analysis for cluster tendency assessment
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Engineering Applications of Artificial Intelligence
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Assessment of clustering tendency is an important first step in cluster analysis. One tool for assessing cluster tendency is the Visual Assessment of Tendency (VAT) algorithm. VAT produces an image matrix that can be used for visual assessment of cluster tendency in either relational or object data. However, VAT becomes intractable for large data sets. The revised VAT (reVAT) algorithm reduces the number of computations done by VAT, and replaces the image matrix with a set of profile graphs that are used for the visual assessment step. Thus, reVAT overcomes the large data set problem which encumbers VAT, but presents a new problem: interpretation of the set of reVAT profile graphs becomes very difficult when the number of clusters is large, or there is significant overlap between groups of objects in the data. In this paper, we propose a new algorithm called bigVAT which (i) solves the large data problem suffered by VAT, and (ii) solves the interpretation problem suffered by reVAT. bigVAT combines the quasi-ordering technique used by reVAT with an image display of the set of profile graphs displaying the clustering tendency information with a VAT-like image. Several numerical examples are given to illustrate and support the new technique.